Learning Graphical Models
A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning. (Wikipedia)
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Generator Identification for Linear SDEs with Additive and Multiplicative Noise
Neural Information Processing Systems
We show that the conditions derived for both types of SDEs are generic.
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Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Trees
Neural Information Processing Systems
We introduce the best order score search (BOSS) and grow-shrink trees (GSTs) for learning directed acyclic graphs (DAGs) in this paradigm.
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IPMix: Label-Preserving Data Augmentation Method for Training Robust Classifiers
Neural Information Processing Systems
Data augmentation has been proven effective for training high-accuracy convolu-tional neural network classifiers by preventing overfitting.
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Distributionally Robust Skeleton Learning of Discrete Bayesian Networks Y eshu Li
Neural Information Processing Systems
The DAG structure of a Bayesian network is typically unknown in practice [Natori et al., 2017,
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